Applying Semantic Technology to Big Data

Rob Styles has written an article regarding how semantic technologies can be effectively applied to the third V of Big Data: variety.

Rob Styles has written an article regarding how semantic technologies can be effectively applied to the third V of Big Data: variety. (The other two Vs are volume and velocity.) Styles writes, “That third V of the Big Data puzzle is where I’ve been helping people use graphs of data (and that’s what RDF is, a graph model). Graphs are great where you have a variety of data that you want to link up. Especially if you want to extend the data often and if you want to extend the data programmatically — i.e. you don’t want to commit to a complete, constraining schema up-front. The other aspect of that variety in data that graphs help with is querying. As Jem Rayfield (BBC News & Sport) explains, using a graph makes the model simpler to develop and query.”

He continues, “Graph data models can reach higher levels of variety in the data before they become unwieldy. This allows more data to be mixed and queried together. Mixing in more data adds more context and more context adds allows for more insight. Insight is what we’re ultimately trying to get at with any data analysis. That’s why the intelligence communities have been using graphs for many years now. What we’re seeing now, with the combination of Big Data and graph technologies, is the ability to add value inside the enterprise. Graphs are useful for data analysis even if you don’t intend to publish the data on the semantic web. Maybe even especially then.”